Search results for "Base"

showing 10 items of 8362 documents

Graph-theoretical derivation of brain structural connectivity

2020

Brain connectivity at the single neuron level can provide fundamental insights into how information is integrated and propagated within and between brain regions. However, it is almost impossible to adequately study this problem experimentally and, despite intense efforts in the field, no mathematical description has been obtained so far. Here, we present a mathematical framework based on a graph-theoretical approach that, starting from experimental data obtained from a few small subsets of neurons, can quantitatively explain and predict the corresponding full network properties. This model also changes the paradigm with which large-scale model networks can be built, from using probabilisti…

0209 industrial biotechnologyTheoretical computer scienceComputer scienceNeuronal network02 engineering and technologyMECHANISMSCENTRALITY020901 industrial engineering & automationSettore MAT/05 - Analisi MatematicaNeuronal networksConnectome0202 electrical engineering electronic engineering information engineeringINDEXComputer Science::DatabasesRandom graphsSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniSettore INF/01 - InformaticaQuantitative Biology::Neurons and CognitionApplied MathematicsProbabilistic logicExperimental data020206 networking & telecommunicationsComputational MathematicsSYNCHRONIZATIONSIMULATIONGraph (abstract data type)Applied Mathematics and Computation
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Beyond cells – The virome in the human holobiont

2019

Viromics, or viral metagenomics, is a relatively new and burgeoning field of research that studies the complete collection of viruses forming part of the microbiota in any given niche. It has strong foundations rooted in over a century of discoveries in the field of virology and recent advances in molecular biology and sequencing technologies. Historically, most studies have deconstructed the concept of viruses into a simplified perception of viral agents as mere pathogens, which demerits the scope of large-scale viromic analyses. Viruses are, in fact, much more than regular parasites. They are by far the most dynamic and abundant entity and the greatest killers on the planet, as well as th…

0209 industrial biotechnologyViral metagenomicsHistorybacteriophagesdatabases02 engineering and technologyReviewBiochemistry Genetics and Molecular Biology (miscellaneous)MicrobiologyApplied Microbiology and BiotechnologyGenetic engineeringtaxonomy020901 industrial engineering & automationVirology0202 electrical engineering electronic engineering information engineeringGeneticsmicrobiotaHuman viromeMolecular Biologylcsh:QH301-705.5020208 electrical & electronic engineeringEnvironmental ethicsCell BiologyHolobiontlcsh:Biology (General)MetagenomicsViral studiesParasitologyviral metagenomicsMicrobial Cell
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Global sensitivity analysis in welding simulations -- what are the material data you really need ?

2011

In this paper, the sensitivity analysis methodology is applied to numerical welding simulation in order to rank the importance of input variables on the outputs of the code like distorsions or residual stresses. The numerical welding simulation uses the finite element method, with a thermal computation followed by a mechanical one. Classically, a local sensitivity analysis is performed, hence the validity of the results is limited to the neighbourhood of a nominal point, and cross effects cannot be detected. This study implements a global sensitivity analysis which allows to screen the whole material space of the steel family mechanical properties. A set of inputs of the mechanical model-ma…

0209 industrial biotechnology[ SPI.MECA.STRU ] Engineering Sciences [physics]/Mechanics [physics.med-ph]/Mechanics of the structures [physics.class-ph]02 engineering and technologyWeldingFinite Element[ PHYS.MECA.STRU ] Physics [physics]/Mechanics [physics]/Mechanics of the structures [physics.class-ph]law.invention020901 industrial engineering & automation[PHYS.MECA.STRU]Physics [physics]/Mechanics [physics]/Structural mechanics [physics.class-ph]0203 mechanical engineeringlawPoint (geometry)Sensitivity (control systems)numerical experimentsMathematicsApplied MathematicsGeneral EngineeringSampling (statistics)Computer Graphics and Computer-Aided DesignFinite element method[SPI.MECA.STRU]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Mechanics of the structures [physics.class-ph]welding simulation020303 mechanical engineering & transportsLatin hypercube samplingRanking[SPI.MECA.STRU]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Structural mechanics [physics.class-ph]Variance-based sensitivity analysisAlgorithmAnalysis
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Abnormal Textures Identification Based on Digital Hilbert Optics Methods: Fundamental Transforms and Models

2017

The article presents the abnormal textures identification technology based on structural and statistical models of amplitude-phase images (APIm) – multidimensional data arrays (semantic models) and statistical correlation analysis methods using the generalized discrete Hilbert transforms (DHT) – 2D Hilbert (Foucault) isotropic (HTI), anisotropic (HTA) and total transforms – AP-analysis (APA) to calculate the APIm. The identified fragments of textures are obtained as examples of experimental observation of real mammograms contains areas of pathological tissues. The DHT based information technology as conceptual chart description is discussed and illustrated with DHO domain images. As additio…

0209 industrial biotechnologybusiness.industryComputer scienceIsotropyStatistical modelPattern recognition02 engineering and technologyBase (topology)Domain (mathematical analysis)030218 nuclear medicine & medical imaging03 medical and health sciencesIdentification (information)020901 industrial engineering & automation0302 clinical medicineComputer visionArtificial intelligenceAnomaly (physics)Anisotropybusiness
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Exploring Virtual Reality as an Integrated Development Environment for Cyber-Physical Systems

2019

Cyber Physical Systems (CPS) development approaches tend to start from the physical (hardware) perspective, and the software is the final element in the process. However, this approach is unfit for the more software-intensive world that is increasingly iterative, connected, and constantly online. Many constraints prevent the application of iterative, incremental, and agile development methodologies, which now are the norm for many other fields of software. Time-consuming system validation can only start when both hardware and software components are ready, which implies that the software delivery and quality is almost always the final bottleneck in the CPS development and integration. Also …

0209 industrial biotechnologybusiness.industryComputer scienceProcess (engineering)Integrated softwareeducationCyber-physical system020207 software engineering02 engineering and technologyVirtualizationcomputer.software_genre113 Computer and information sciencesBottleneck020901 industrial engineering & automationSoftwareComponent-based software engineering0202 electrical engineering electronic engineering information engineeringbusinessSoftware engineeringcomputerAgile software development
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Do Randomized Algorithms Improve the Efficiency of Minimal Learning Machine?

2020

Minimal Learning Machine (MLM) is a recently popularized supervised learning method, which is composed of distance-regression and multilateration steps. The computational complexity of MLM is dominated by the solution of an ordinary least-squares problem. Several different solvers can be applied to the resulting linear problem. In this paper, a thorough comparison of possible and recently proposed, especially randomized, algorithms is carried out for this problem with a representative set of regression datasets. In addition, we compare MLM with shallow and deep feedforward neural network models and study the effects of the number of observations and the number of features with a special dat…

0209 industrial biotechnologyrandom projectionlcsh:Computer engineering. Computer hardwareComputational complexity theoryComputer scienceRandom projectionlcsh:TK7885-789502 engineering and technologyMachine learningcomputer.software_genresupervised learningapproximate algorithmsSet (abstract data type)regressioanalyysi020901 industrial engineering & automationdistance–based regressionalgoritmit0202 electrical engineering electronic engineering information engineeringordinary least–squaresbusiness.industrySupervised learningsingular value decompositionminimal learning machineMultilaterationprojektioRandomized algorithmkoneoppiminenmachine learningScalabilityFeedforward neural network020201 artificial intelligence & image processingArtificial intelligenceapproksimointibusinesscomputerMachine Learning and Knowledge Extraction
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Using Inverse Reinforcement Learning with Real Trajectories to Get More Trustworthy Pedestrian Simulations

2020

Reinforcement learning is one of the most promising machine learning techniques to get intelligent behaviors for embodied agents in simulations. The output of the classic Temporal Difference family of Reinforcement Learning algorithms adopts the form of a value function expressed as a numeric table or a function approximator. The learned behavior is then derived using a greedy policy with respect to this value function. Nevertheless, sometimes the learned policy does not meet expectations, and the task of authoring is difficult and unsafe because the modification of one value or parameter in the learned value function has unpredictable consequences in the space of the policies it represents…

0209 industrial biotechnologyreinforcement learningComputer scienceGeneral Mathematics02 engineering and technologypedestrian simulationTask (project management)learning by demonstration020901 industrial engineering & automationAprenentatgeInformàticaBellman equation0202 electrical engineering electronic engineering information engineeringComputer Science (miscellaneous)Reinforcement learningEngineering (miscellaneous)business.industrycausal entropylcsh:MathematicsProcess (computing)020206 networking & telecommunicationsFunction (mathematics)inverse reinforcement learninglcsh:QA1-939Problem domainTable (database)Artificial intelligenceTemporal difference learningbusinessoptimizationMathematics
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Practical Calculation Models for Column Footing and Comparison with Experimental Data

2017

In this paper, a simplified calculation model for the prediction of the load-carrying capacity of an RC column footing with a square cross section is presented. A detailed background of available experimental data and existing models for the prediction of the load-carrying capacity of slender and deep footings is presented. Cases of flexural failure and punching shear failures for slender footing and concrete strut crushing and tie yielding in deep members are analyzed. The aim of the paper is to propose a simple design formula for slender and deep footing verified by available experimental data and in agreement with other existing expressions. Expressions of the maximum mechanical ratio of…

0211 other engineering and technologiesExperimental dataFooting020101 civil engineeringPunching shear02 engineering and technologyBuilding and ConstructionMechanicsColumn (database)0201 civil engineeringBeam modelElastic soilArts and Humanities (miscellaneous)Concrete crushingStrut-and-tie model021105 building & constructionGeologyCivil and Structural EngineeringPractice Periodical on Structural Design and Construction
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Strategies of Identification of a Base-Isolated Hospital Building by Coupled Quasi-Static and Snap-Back Tests

2020

In this paper, the description of a series of quasi-static pushing tests and dynamic snap-back tests is proposed, involving the base-isolated emergency building of the Palermo university hospital. The base isolation system is characterized by a set of double-curved friction pendulum isolators placed on the top of the columns of the underground level, characteristics that cannot be found in the experimental studies available in the literature. The aim of the work was to investigate the static and dynamic properties of the building in question and comparing the in-situ results with the characteristics assigned during the design process and to assess the level of agreement. Static lateral push…

021110 strategic defence & security studiesComputer sciencebusiness.industry0211 other engineering and technologies020101 civil engineering02 engineering and technologyBuilding and ConstructionStructural engineeringGeotechnical Engineering and Engineering GeologyUniversity hospitalBase (topology)0201 civil engineeringSettore ICAR/09 - Tecnica Delle CostruzioniSettore ING-IND/31 - ElettrotecnicaSnap backIdentification (information)friction friction pendulum bearing full-scale experimental tests in-situ testing seismic isolation snap-back testSeismic isolationbusinessQuasistatic processCivil and Structural EngineeringJournal of Earthquake Engineering
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Modelling Rainfall-induced Shallow Landslides at Different Scales Using SLIP - Part II

2016

Abstract This paper (Part II) is companion of another one published in this Conference (Part I). Both the papers describe the approach followed in the application of the SLIP model at different scales to foresee the triggering mechanism of rainfall-induced shallow landslides. In particular, this paper (Part II) focuses on the modeling at medium and large scale (regional and national level). The possibility of using the same means to model the phenomenon from the scale of the representative elementary volume (i.e. flume laboratory tests) to the medium and large scale (hundreds or thousands square kilometers wide areas) allowed from the one hand to strengthen the model assumptions and on the …

021110 strategic defence & security studiesEngineeringbusiness.industryrainfallShallow landslides0211 other engineering and technologiesLandslide02 engineering and technologyGeneral MedicineSlip (materials science)010502 geochemistry & geophysics01 natural sciencesphysically-based modelFlumeslope stability analysisRepresentative elementary volumeGeotechnical engineeringNational levelbusinessSlope stability analysisEngineering(all)Seismology0105 earth and related environmental sciencesProcedia Engineering
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